Stacey N. Reinke

The application of artificial neural networks in metabolomics: a historical perspective

Kevin M. Mendez, David I. Broadhurst, Stacey N. Reinke, 2019, Metabolomics.

Migrating from partial least squares discriminant analysis to artificial neural networks: a comparison of functionally equivalent visualisation and feature contribution tools using jupyter notebooks

Kevin M. Mendez, David I. Broadhurst, Stacey N. Reinke, 2020, Metabolomics.

Introducing Undergraduate Students to Metabolomics Using Liquid Chromatography–High Resolution Mass Spectrometry Analysis of Horse Blood

Mary C. Boyce, Stacey N. Reinke, Nathan G. Lawler, 2019, Journal of Chemical Education.

Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing

Leighton Pritchard, Kevin M. Mendez, David I. Broadhurst, 2019, Metabolomics.

A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification

Kevin M. Mendez, David I. Broadhurst, Stacey N. Reinke, 2019, Metabolomics.

Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies

Ian D. Wilson, Royston Goodacre, David Broadhurst, 2018, Metabolomics : Official journal of the Metabolomic Society.

Metabolomics analysis identifies different metabotypes of asthma severity

Stacey N. Reinke, Alexander Fauland, R. Djukanović, 2017, European Respiratory Journal.